Numerous computational methods have been developed for DTI prediction problem. Similarity-based methods such as kernel regression [1]} and matrix factorization [2]} methods exploit known DTI's drug-target similarity information and infer new ones. However, these methods are shown to be not generalizable to different protein classes [3]}. Feature-based methods feed numerical descriptors of drug and proteins into downstream prediction models. Popular numerical descriptors include ECFP [4]} and PubChem [5]} for drugs, CTD [6]} and PSC [7]} for proteins. Classic machine learning methods such as gradient boosting [8]} have shown promises in predictive performance. Recently, deep learning based methods [9]}, [3]}, [11]}, [12]} have shown further improvement of performance due to its capability to capture complex non-linear signals of DTI. MolTrans differs from existing works with (1) its knowledge-driven model architecture design rather than direct application of existing deep learning models; (2) emphasis on interpretability instead of predictive performance alone to potentially aid medical chemists for better decision making; (3) usage of external drug and target data to complement interaction dataset.
